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Accurately detecting sarcasm and context in sentiment analysis is challenging due to the following reasons:
Implicit Meaning: Sarcasm often conveys the opposite of the literal meaning, making it difficult for models that rely on surface-level text analysis to detect true intent. For example, "Oh, great! Another traffic jam!" appears positive but is actually negative.
Lack of Context: Sentiment analysis models typically analyze isolated text without broader conversational or situational context, limiting their ability to understand nuanced expressions.
Cultural and Linguistic Variations: Sarcasm varies across cultures and languages, making it harder for models trained on one dataset to generalize across diverse contexts.
Absence of Vocal and Visual Cues: Text lacks tone, pitch, and facial expressions, which are essential for identifying sarcasm in spoken communication.
Subtle Language Use: The use of irony, ambiguous words, or contradictory statements can confuse models that rely heavily on keywords for sentiment classification.
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First thing that comes to my mind is that in sarcasm we often use positive words to express something negative (or vice versa) š§ Which can be hard for a model to detect accurately. This leads me to my second thought: There must be high quality datasets for training